

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case				:      Optional[int]   		=			logging.get_logger(__name__)

__snake_case				:      int   		=			{"""vocab_file""": """spiece.model"""}

__snake_case				:      List[str]   		=			{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__snake_case				:      Optional[Any]   		=			{
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				int       					=			VOCAB_FILES_NAMES
						_A    :				Optional[Any]       					=			PRETRAINED_VOCAB_FILES_MAP
						_A    :				int       					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A    :				Any       					=			['''input_ids''', '''attention_mask''']
						_A    :				List[int]       					=			[]





						def __init__(    self  ,      _lowercase  ,      _lowercase="<unk>"  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="<pad>"  ,      _lowercase="[SEP]"  ,      _lowercase="[MASK]"  ,      _lowercase="[CLS]"  ,      _lowercase = None  ,      **_lowercase  ,      )     -> None:
									a_						:						List[Any] 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else bos_token
									a_						:						Union[str, Any] 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else eos_token
									a_						:						str 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else unk_token
									a_						:						str 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else pad_token
									a_						:						List[str] 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else cls_token
									a_						:						List[Any] 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else sep_token

									# Mask token behave like a normal word, i.e. include the space before it
									a_						:						Union[str, Any] 							= AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase    ) if isinstance(_lowercase  ,      _lowercase    ) else mask_token

									a_						:						str 							= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_lowercase  ,      eos_token=_lowercase  ,      unk_token=_lowercase  ,      pad_token=_lowercase  ,      sep_token=_lowercase  ,      mask_token=_lowercase  ,      cls_token=_lowercase  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **_lowercase  ,      )

									a_						:						str 							= vocab_file

									a_						:						str 							= spm.SentencePieceProcessor(**self.sp_model_kwargs    )
									self.sp_model.Load(_lowercase    )





						@property
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									return self.sp_model.get_piece_size()





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						Tuple 							= {self.convert_ids_to_tokens(_lowercase    ): i for i in range(self.vocab_size    )}
									vocab.update(self.added_tokens_encoder    )
									return vocab





						def __getstate__(    self    )     -> Any:
									a_						:						str 							= self.__dict__.copy()
									a_						:						Tuple 							= None
									return state





						def __setstate__(    self  ,      _lowercase    )     -> Union[str, Any]:
									a_						:						int 							= d

									# for backward compatibility
									if not hasattr(self  ,      """sp_model_kwargs"""    ):
												a_						:						int 							= {}

									a_						:						Union[str, Any] 							= spm.SentencePieceProcessor(**self.sp_model_kwargs    )
									self.sp_model.Load(self.vocab_file    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[str]:
									return self.sp_model.encode(_lowercase  ,      out_type=_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Dict:
									return self.sp_model.piece_to_id(_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Union[str, Any]:
									a_						:						Optional[Any] 							= self.sp_model.IdToPiece(_lowercase    )
									return token





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Union[str, Any]:
									a_						:						List[Any] 							= []
									a_						:						Optional[Any] 							= """"""
									a_						:						int 							= False
									for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
															if not prev_is_special:
																		out_string += " "
															out_string += self.sp_model.decode(_lowercase    ) + token
															a_						:						str 							= True
															a_						:						Union[str, Any] 							= []
												else:
															current_sub_tokens.append(_lowercase    )
															a_						:						Tuple 							= False
									out_string += self.sp_model.decode(_lowercase    )
									return out_string.strip()





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = False  ,      _lowercase = None  ,      _lowercase = True  ,      **_lowercase  ,      )     -> str:
									a_						:						int 							= kwargs.pop("""use_source_tokenizer"""  ,      _lowercase    )

									a_						:						str 							= self.convert_ids_to_tokens(_lowercase  ,      skip_special_tokens=_lowercase    )

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									a_						:						str 							= []
									a_						:						Optional[int] 							= []
									for token in filtered_tokens:
												if skip_special_tokens and token in self.all_special_ids:
															continue
												if token in self.added_tokens_encoder:
															if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(_lowercase    )    )
																		a_						:						List[Any] 							= []
															sub_texts.append(_lowercase    )
												else:
															current_sub_text.append(_lowercase    )
									if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(_lowercase    )    )

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
												a_						:						Dict 							= re.sub(r""" (\[(MASK|SEP)\])"""  ,      r"""\1"""  ,      """ """.join(_lowercase    )    )
									else:
												a_						:						Optional[Any] 							= """""".join(_lowercase    )

									a_						:						Tuple 							= (
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
												a_						:						Union[str, Any] 							= self.clean_up_tokenization(_lowercase    )
												return clean_text
									else:
												return text





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> Tuple[str]:
									if not os.path.isdir(_lowercase    ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
												return
									a_						:						Any 							= os.path.join(
									    _lowercase  ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

									if os.path.abspath(self.vocab_file    ) != os.path.abspath(_lowercase    ) and os.path.isfile(self.vocab_file    ):
												copyfile(self.vocab_file  ,      _lowercase    )
									elif not os.path.isfile(self.vocab_file    ):
												with open(_lowercase  ,      """wb"""    ) as fi:
															a_						:						Tuple 							= self.sp_model.serialized_model_proto()
															fi.write(_lowercase    )

									return (out_vocab_file,)





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> List[int]:
									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									a_						:						Dict 							= [self.cls_token_id]
									a_						:						str 							= [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = False    )     -> List[int]:
									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_lowercase  ,      token_ids_a=_lowercase  ,      already_has_special_tokens=_lowercase    )

									if token_ids_a is None:
												return [1] + ([0] * len(_lowercase    )) + [1]
									return [1] + ([0] * len(_lowercase    )) + [1] + ([0] * len(_lowercase    )) + [1]





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None    )     -> List[int]:
									a_						:						int 							= [self.sep_token_id]
									a_						:						str 							= [self.cls_token_id]
									if token_ids_a is None:
												return len(cls + token_ids_a + sep    ) * [0]
									return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			with open(a__) as metadata_file:
						a_						:						Any 							= json.load(a__)
			a_						:						Dict 							= LukeConfig(use_entity_aware_attention=a__  ,	**metadata["""model_config"""])

			# Load in the weights from the checkpoint_path
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")

			# Load the entity vocab file
			a_						:						List[str] 							= load_entity_vocab(a__)

			a_						:						int 							= RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""])

			# Add special tokens to the token vocabulary for downstream tasks
			a_						:						Optional[Any] 							= AddedToken("""<ent>"""  ,	lstrip=a__  ,	rstrip=a__)
			a_						:						int 							= AddedToken("""<ent2>"""  ,	lstrip=a__  ,	rstrip=a__)
			tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]})
			config.vocab_size += 2

			print(f'''Saving tokenizer to {pytorch_dump_folder_path}''')
			tokenizer.save_pretrained(a__)
			with open(os.path.join(a__  ,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""])  ,	"""w""") as f:
						json.dump(a__  ,	a__)

			a_						:						List[str] 							= LukeTokenizer.from_pretrained(a__)

			# Initialize the embeddings of the special tokens
			a_						:						Optional[int] 							= state_dict["""embeddings.word_embeddings.weight"""]
			a_						:						List[str] 							= word_emb[tokenizer.convert_tokens_to_ids(["""@"""])[0]].unsqueeze(0)
			a_						:						List[Any] 							= word_emb[tokenizer.convert_tokens_to_ids(["""#"""])[0]].unsqueeze(0)
			a_						:						Optional[Any] 							= torch.cat([word_emb, ent_emb, enta_emb])

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers):
						for matrix_name in ["query.weight", "query.bias"]:
									a_						:						Any 							= f'''encoder.layer.{layer_index}.attention.self.'''
									a_						:						List[str] 							= state_dict[prefix + matrix_name]
									a_						:						List[Any] 							= state_dict[prefix + matrix_name]
									a_						:						Dict 							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			a_						:						str 							= state_dict["""entity_embeddings.entity_embeddings.weight"""]
			a_						:						int 							= entity_emb[entity_vocab["""[MASK]"""]]

			a_						:						int 							= LukeModel(config=a__).eval()

			a_							,					a_						:						Optional[int] 							= model.load_state_dict(a__  ,	strict=a__)
			if not (len(a__) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f'''Missing keys {", ".join(a__)}. Expected only missing embeddings.position_ids''')
			if not (all(key.startswith("""entity_predictions""") or key.startswith("""lm_head""") for key in unexpected_keys)):
						raise ValueError(
						    """Unexpected keys"""
						    f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions") or key.startswith("lm_head"))])}''')

			# Check outputs
			a_						:						List[Any] 							= LukeTokenizer.from_pretrained(a__  ,	task="""entity_classification""")

			a_						:						str 							= (
			    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
			    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
			)
			a_						:						str 							= (3_9, 4_2)
			a_						:						Tuple 							= tokenizer(a__  ,	entity_spans=[span]  ,	add_prefix_space=a__  ,	return_tensors="""pt""")

			a_						:						List[str] 							= model(**a__)

			# Verify word hidden states
			if model_size == "large":
						a_						:						Optional[int] 							= torch.Size((1, 4_2, 1_0_2_4))
						a_						:						List[str] 							= torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
			else:  # base
						a_						:						List[str] 							= torch.Size((1, 4_2, 7_6_8))
						a_						:						str 							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						a_						:						Dict 							= torch.Size((1, 1, 1_0_2_4))
						a_						:						int 							= torch.tensor([[0.0466, -0.0106, -0.0179]])
			else:  # base
						a_						:						Optional[Any] 							= torch.Size((1, 1, 7_6_8))
						a_						:						str 							= torch.tensor([[0.1457, 0.1044, 0.0174]])

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
						    f''' {expected_shape}''')
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("""Saving PyTorch model to {}""".format(a__))
			model.save_pretrained(a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= {}
			with open(a__  ,	"""r"""  ,	encoding="""utf-8""") as f:
						for index, line in enumerate(a__):
									a_							,					a_						:						List[Any] 							= line.rstrip().split("""\t""")
									a_						:						Any 							= index

			return entity_vocab


if __name__ == "__main__":
	__snake_case				:      Tuple   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
	parser.add_argument(
	    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
	)
	parser.add_argument(
	    """--entity_vocab_path""",
	    default=None,
	    type=str,
	    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
	)
	parser.add_argument(
	    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
	)
	__snake_case				:      List[str]   		=			parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)


